Ireland / Young Solar Image Analysis and Visualization

1. Auflage 2009
ISBN: 978-0-387-98154-3
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 289 Seiten

The SECCHI A and B instrument suites (Howard et al. , 2006) onboard the two STEREO mission spacecraft (Kaiser, 2005) are each composed of: one Extreme Ultra-Violet Imager (EUVI), two white-light coronagraphs (COR1 and COR2), and two wide-angle heliospheric imagers (HI1 and HI2). Technical descriptions of EUVI, COR1 and the HIs can be found in Wuelser et al. (2004), Thompson et al. (2003), and De?se et al. (2003), respectively. The images produced by SECCHI represent a data visualization challenge: i) the images are 2048×2048 pixels (except for the HIs, which are usually binned onboard 2×2), thus the vast majority of computer displays are not able to display them at full frame and full r- olution, and ii) more importantly, the ?ve instruments of SECCHI A and B were designed to be able to track Coronal Mass Ejections from their onset (with EUVI) to their pro- gation in the heliosphere (with the HIs), which implies that a set of SECCHI images that covers the propagation of a CME from its initiation site to the Earth is composed of im- ?1 ages with very different spatial resolutions - from 1. 7 arcsecondspixel for EUVI to 2. 15 ?1 arcminutespixel for HI2, i. e. 75 times larger. A similar situation exists with the angular scales of the physical objects, since the size of a CME varies by orders of magnitude as it expands in the heliosphere.

Autoren/Hrsg.

Ireland, Jack

Young, C. Alex

Inhaltsverzeichnis

1;Preface: A Topical Issue on Solar Image Analysis and Visualization;6
2;FESTIVAL: A Multiscale Visualization Tool for Solar Imaging Data;7
2.1;Abstract;7
2.2;Introduction;8
2.3;FESTIVAL Basics;8
2.3.1;Layers;8
2.3.2;Systems of Coordinates and Projections;9
2.3.3;Data Handling;9
2.3.4;Coalignment Calibration;10
2.3.5;The Interface;14
2.3.6;Navigation in Time;14
2.3.7;Filters and Enhancements;15
2.3.8;Graphical Output;16
2.3.8.1;Images;16
2.3.8.2;Movies;16
2.4;3D Visualization;16
2.5;Perspectives;17
2.6;Acknowledgements;18
2.7;References;18
3;Visualization of Distributed Solar Data and Metadata with the Solar Weather Browser;19
3.1;Abstract;19
3.2;Introduction;19
3.3;SWB Concept;21
3.4;Server Design;23
3.5;Client Interface;24
3.6;Conclusions;25
3.7;Appendix;26
3.8;Acknowledgements;26
3.9;References;26
4;Widespread Occurrence of Trenching Patterns in the Granulation Field: Evidence for Roll Convection?;27
4.1;Abstract;27
4.2;Introduction;27
4.3;The Data;29
4.4;Image Processing;30
4.5;Results;31
4.6;Conclusion;37
4.7;Acknowledgements;38
4.8;References;39
5;Principal Components and Independent Component Analysis of Solar and Space Data;40
5.1;Abstract;40
5.2;Introduction;40
5.3;Principal Components Analysis;41
5.3.1;Mathematical Background;41
5.3.2;Application of PCA to Heliospheric and Solar Data;43
5.3.2.1;Solar Wind IMF Longitude;43
5.3.2.2;Wilcox Solar Observatory Coronal 3.25R Magnetic Carrington Maps;44
5.4;Independent Component Analysis;46
5.4.1;Mathematical Background;46
5.4.2;Application to Search for Active Longitudes;48
5.4.2.1;Data Preparation and PCA;48
5.4.2.2;Independent Components;51
5.5;Summary and Conclusions;52
5.6;Acknowledgements;53
5.7;References;53
6;Automatic Recognition and Characterisation of Supergranular Cells from Photospheric Velocity Fields;55
6.1;Abstract;55
6.2;Introduction;55
6.3;Cell Analysis Method;56
6.3.1;Data Preparation;56
6.3.2;Overview;57
6.3.3;Method in Detail;58
6.4;Application to Test Data;59
6.5;Application to Real Data;61
6.5.1;Cell Sizes;62
6.5.2;Cell Internal Speeds;63
6.5.3;Tracking Supergranular Cells over Time;65
6.6;Cautionary Notes;65
6.7;Conclusions;66
6.8;Acknowledgements;67
6.9;References;67
7;Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images;68
7.1;Abstract;68
7.2;Introduction;68
7.3;Data Description;69
7.4;Sunspot Detection and Grouping;70
7.4.1;Preprocessing of MDI Images;70
7.4.2;Initial Detection of Solar Features;73
7.4.3;Deciding Active Regions and Grouping of Sunspots;73
7.5;McIntosh Classification of Sunspot Regions;77
7.5.1;Computing the Modified Zürich Class - Z;78
7.5.2;Determining the Type of the Largest Spot - p;78
7.5.3;Determining the Sunspot Distribution - c;80
7.6;Implementation and Evaluation;80
7.6.1;Practical Implementation of the System;80
7.6.2;The Evaluation of the ASC;81
7.7;Discussions, Conclusions and Future Work;83
7.7.1;Discussions and Concluding Remarks;83
7.7.2;Future Work;85
7.8;Acknowledgements;86
7.9;Appendix: Neural Networks;86
7.10;References;87
8;Multifractal Properties of Evolving Active Regions;88
8.1;Abstract;88
8.2;Introduction;89
8.3;Observations and Data Reduction;90
8.4;Fractals and Multifractals;90
8.5;Results;92
8.5.1;Theoretical fractals;92
8.5.2;NOAA 10488;92
8.5.3;NOAA 10798;94
8.5.4;NOAA 10763;96
8.5.5;NOAA 10727;96
8.6;Discussion and Conclusions;97
8.7;Acknowledgements;100
8.8;References;100
9;Multiscale Analysis of Active Region Evolution;101
9.1;Abstract;101
9.2;Introduction;102
9.3;Observations and Data Reduction;103
9.4;A Wavelet Method for Energy Spectrum Extraction;103
9.5;Results;105
9.5.1;Application to a Simple 1D Signal;105
9.5.2;Application to Simulated Magnetograms;105
9.5.3;Application to MDI Magnetograms;105
9.5.3.1;NOAA 09077: A Comparison;105
9.5.3.2;NOAA 10488: Evidence of an Inverse Cascade;106
9.6;Conclusions;112
9.7;Acknowledgements;112
9.8;References;112
10;A Comparison of Feature Classification Methods for Modeling Solar Irradiance Variation;113
10.1;Abstract;113
10.2;Introduction;114
10.3;Observations and Feature Identification Methods;115
10.4;Comparison of Feature Labelings;119
10.5;Discussion;124
10.6;Acknowledgements;125
10.7;References;126
11;The Observed Long- and Short-Term Phase Relation between the Toroidal and Poloidal Magnetic Fields in Cycle 23;128
11.1;Abstract;128
11.2;Introduction;129
11.3;Data Description and Statistical Tool;130
11.3.1;Solar Feature Catalogue;130
11.3.2;Wilcox Solar Observatory Data;131
11.3.3;Some Elements of Statistics ;131
11.4;Results and Discussion;132
11.4.1;Latitudinal Distributions of Sunspot and Active Region Areas and Excess Magnetic Flux;132
11.4.1.1;Quasi-3D Butterfly Diagrams of the Sunspot Areas and Excess Magnetic Field;132
11.4.1.2;The North-South Asymmetry in the Areas and Excess Magnetic Flux;134
11.4.2;Correlation with the Background Solar Magnetic Field;136
11.4.2.1;Latitudinal Variations of the Background SMF;137
11.4.2.2;The Long-Term Phase Relation between the Sunspot Excess Magnetic Flux and the SMF;138
11.4.2.3;The Short-Term Phase Relation between the SMF and the Sunspot EMF;139
11.5;Conclusions;145
11.6;Acknowledgements;146
11.7;References;146
12;Comparison of Five Numerical Codes for Automated Tracing of Coronal Loops;148
12.1;Abstract;148
12.2;Introduction;149
12.3;Automated Loop-Tracing Codes;149
12.3.1;The Oriented-Connectivity Method;150
12.3.2;The Dynamic Aperture-based Loop Segmentation Method;150
12.3.3;Unbiased Detection of Curvilinear Structures Method (UDM);151
12.3.4;Oriented-Directivity Loop Tracing Method;151
12.3.5;Ridge Detection by Automated Scaling ;152
12.4;Test Comparisons of Loop-Tracing Codes;152
12.4.1;Test Image and High-Pass Filtering;153
12.4.2;Manual Tracing of Loops;154
12.4.3;Automated Tracing of Loops;154
12.4.3.1;Automated Tracing with the OCM Code;156
12.4.3.2;Automated Tracing with the DAM Code;156
12.4.3.3;Automated Tracing with the UDM Code;157
12.4.3.4;Automated Tracing with the ODM Code;157
12.4.3.5;Automated Tracing with the RAS Code;158
12.4.4;Quantitative Comparison of Automated Tracing Codes;159
12.4.4.1;Cumulative Distribution of Loop Lengths;159
12.4.4.2;Maximum Detected Loop Lengths;160
12.4.4.3;Completeness of Loop Detection;161
12.4.4.4;Accuracy and Sensitivity of Loop Detection;161
12.4.4.5;Computation Speed of Automated Tracing Codes;163
12.5; Discussion and Conclusions ;164
12.6;Acknowledgements;165
12.7;References;165
13;Segmentation of Loops from Coronal EUV Images;167
13.1;Abstract;167
13.2;Introduction;167
13.3;Method;168
13.3.1;Ridgel Location and Orientation;169
13.3.2;Ridgel Connection to Chains;172
13.3.3;Curve Fits to the Ridgel Chains;175
13.4;Application;177
13.5;Discussion;180
13.6;Acknowledgements;180
13.7;References;181
14;The Pixelised Wavelet Filtering Method to Study Waves and Oscillations in Time Sequences of Solar Atmospheric Images;182
14.1;Abstract;182
14.2;Introduction;182
14.3;Test Signals;184
14.4;The Scheme of the Pixelised Wavelet Filtering Method;185
14.5;Application to Sunspot Oscillations;189
14.6;Application to Coronal Loop Oscillations;191
14.7;Conclusions;192
14.8;Acknowledgements;194
14.9;References;194
15;A Time-Evolving 3D Method Dedicated to the Reconstruction of Solar Plumes and Results Using Extreme Ultraviolet Data;196
15.1;Abstract;196
15.2;Introduction;197
15.3;Method;198
15.3.1;Direct Problem;198
15.3.2;Modeling of the Temporal Evolution;199
15.3.3;Inverse Problem;200
15.3.4;Criterion Minimization;201
15.3.5;Descent Direction Definition and Stop Threshold;201
15.4;Method Validation;202
15.4.1;Simulation Generation Process;202
15.4.2;Analysis of Results;202
15.4.3;Choice of Evolution Areas;204
15.5;Reconstruction of SOHO/EIT Data;205
15.5.1;Data Preprocessing;205
15.5.2;Analysis of Results;205
15.6;Discussion;207
15.7;Conclusion;208
15.8;Acknowledgements;209
15.9;Appendix A: Pseudo-Inverse Minimization;209
15.10;Appendix B: Gradient-Like Method;209
15.11;References;210
16;Automatic Detection and Classification of Coronal Holes and Filaments Based on EUV and Magnetogram Observations of the Solar Disk;211
16.1;Abstract;211
16.2;Introduction;212
16.3;Overview of Approach and Data Selection;213
16.4;Data Preparation and Image Processing;214
16.5;Candidate Features: Detection and Classification Method;215
16.6;Candidate Features: Final Product;220
16.7;Discussion and Conclusion;224
16.8;Acknowledgements;225
16.9;References;225
17;Spatial and Temporal Noise in Solar EUV Observations;226
17.1;Abstract;226
17.2;Introduction;227
17.3;EIT Data Set Analysis;227
17.3.1;Sources of Noise in EIT Images;228
17.3.1.1;Poisson Noise;228
17.3.1.2;Blurring from the PSF;228
17.3.1.3;Flat Field;229
17.3.1.4;Read-out Noise;229
17.3.2;Method;229
17.3.3;Regularity Analysis Using Pointwise Hölder Exponents;230
17.3.4;Signal-to-Noise Ratio and Relationship between Mean and Standard Deviation;231
17.4;Forward Modeling Approach;233
17.4.1;Generation of the Data Sequence;234
17.4.2;Phenomenological Model;236
17.4.3;Results of the Simulation Study;237
17.5;Discussion and Conclusion;238
17.6;Acknowledgements;239
17.7;Appendix: Pointwise Hölder Exponent;239
17.8;References;239
18;Multiscale Edge Detection in the Corona;241
18.1;Abstract;241
18.2;Introduction;241
18.3;A System for Automatic CME Detection;242
18.4;Observations;243
18.5;Methodology;244
18.5.1;Edge Detection;244
18.5.2;Multiscale Edge Detection;245
18.5.3;Edge Selection and Error Estimation;246
18.6;Results;248
18.7;Conclusions and Future Work;252
18.8;Acknowledgements;252
18.9;References;252
19;Automated Prediction of CMEs Using Machine Learning of CME-Flare Associations ;254
19.1;Abstract;254
19.2;Introduction;254
19.3;CMEs and their Associations with Solar Activities and Features;256
19.4;The Computer Platform Design for CME Predictions;256
19.4.1;Associating Flares and CMEs;257
19.4.2;Creating the Associated Numerical Data Set;257
19.5;Practical Implementation and Results;258
19.5.1;The Learning Algorithms and Techniques;258
19.5.2;Optimising the Learning Algorithms;259
19.5.2.1;Optimising the CCNN;259
19.5.2.2;Optimising the SVM;261
19.5.3;Comparing the Prediction Performances;262
19.5.4;Further Investigation of Catalogue Data;264
19.6;Conclusions and Future Research;264
19.7;Acknowledgements;265
19.8;References;266
20;Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series;267
20.1;Abstract;267
20.2;Introduction;268
20.3;Methodology;269
20.3.1;Preprocessing;269
20.3.2;Initial Detection;271
20.3.3;Tracking;273
20.3.4;Leading-Edge Determination and Visualization;275
20.3.5;Example Case;275
20.4;Results and Validation;276
20.4.1;Results;276
20.4.2;Validation and Comparison;277
20.5;Discussion and Conclusion;280
20.6;Acknowledgements;281
20.7;References;281